Dimensionality Reduction Techniques Quiz With Answers

________ is a technique for reducing dimensionality while preserving data locality.

________ is used to find a linear transformation that maximizes class separability.

________ focuses on preserving the local neighborhood structure of data points.

________ is effective for dimensionality reduction in datasets with high-dimensional sparse features.

________ seeks a low-dimensional representation by minimizing reconstruction error.

________ is an extension of PCA that handles non-linear relationships.

________ is effective for reducing dimensions in text data analysis.

________ is a graph-based technique that preserves graph distances.

Principal Component Analysis (PCA) is used for _________.

Isomap is a technique that preserves ________ distances in the reduced space.

________ is a technique for reducing dimensionality while maintaining pairwise distances.

________ emphasizes the reconstruction of the original data from its reduced dimensions.

________ is useful for finding a low-dimensional representation of text data.

________ tries to find a low-dimensional representation that preserves local structure.

________ focuses on feature extraction rather than feature selection.

________ is used for dimensionality reduction in text mining and natural language processing.

________ is known for its ability to capture non-linear relationships in data.

________ seeks a low-dimensional representation that maintains the pairwise distances between points.

________ techniques are effective for reducing redundancy and noise in data.

________ is a technique for reducing the dimensionality of data by finding a low-rank approximation.

________ is a dimensionality reduction technique that uses the covariance matrix of the data.

The objective of Laplacian Eigenmaps is to preserve ________ in the reduced space.

________ focuses on finding a linear combination of features that maximizes variance.

________ is used to reduce the dimensionality of data while preserving information about the class labels.

________ transforms variables into a new set of uncorrelated variables.

________ is known for its ability to capture sparse and localized patterns.

________ is a probabilistic approach to dimensionality reduction.

Multidimensional Scaling (MDS) is used to _________.

________ is suitable for reducing dimensionality while preserving data relationships in a graph.

In PCA, the principal components are determined by maximizing _________.

Singular Value Decomposition (SVD) is a technique used in _________.

________ focuses on preserving the maximum variance in the data.

________ is used to reduce the dimensionality of data while preserving the global structure.

________ is suitable for dimensionality reduction in high-dimensional data with complex relationships.

t-SNE (t-Distributed Stochastic Neighbor Embedding) is effective for ________.

________ is particularly useful for datasets with non-linear manifolds.

Linear Discriminant Analysis (LDA) focuses on maximizing ________ between classes.

________ techniques focus on extracting useful features from high-dimensional data.

The purpose of Non-Negative Matrix Factorization (NMF) is to _________.

________ is commonly used for reducing dimensionality in image processing tasks.